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😎 up-to-date & curated list of awesome Attacks on Large-Vision-Language-Models papers, methods & resources.
| Date | Stars |
|---|---|
| 2026-07-31 | 566 |
| 2026-08-01 | 566 |
| 2026-08-06 | 566 |
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# Awesome-LVLM-Attack [](https://github.com/sindresorhus/awesome) A **continual** collection of papers related to Attacks on Large-Vision-Language-Models (LVLMs). Large vision-language models (LVLMs) have achieved significant success and demonstrated promising capabilities in various multimodal downstream tasks. Despite their remarkable capabilities, the increased complexity and deployment of LVLMs have also exposed them to various security threats and vulnerabilities, making the study of attacks on these models a critical area of research. Here, we've summarized existing LVLM Attack methods in our survey paper👍. [A Survey of Attacks on Large Vision-Language Models: Resources, Advances, and Future Trends](https://arxiv.org/abs/2407.07403) > If you find some important work missed, it would be super helpful to let me know (`[email protected]`). Thanks! > If you find our survey useful for your research, please consider citing: ``` @article{liu2025survey, title={A survey of attacks on large vision--language models: Resources, advances, and future trends}, author={Liu, Daizong and Yang, Mingyu and Qu, Xiaoye and Zhou, Pan and Cheng, Yu and Hu, Wei}, journal={IEEE Transactions on Neural Networks and Learning Systems}, year={2025}, publisher={IEEE} } ``` **Table of Contents** - [Adversarial Attacks](#Adversarial-Attack) - [Jailbreak Attacks](#Jailbreak-Attack) - [Prompt Injection](#Prompt-Injection) - [Data Poisoning](#Data-Poisoning) - [Special Attacks For LVLM Applications](#Special-Attacks-For-LVLM-Applications) - [Benchmarks](#Benchmarks) <div align=center><img src="./img/four_attacks.png" width="90%" height="90%" /></div> --- ## Adversarial-Attack * **On the Adversarial Robustness of Multi-Modal Foundation Models** | * Christian Schlarmann, Matthias Hein * University of Tubingen * [ICCVworkshop2023] https://openaccess.thecvf.com/content/ICCV2023W/AROW/papers/Schlarmann_On_the_Adversarial_Robustness_of_Multi-Modal_Foundation_Models_ICCVW_2023_paper.pdf * **On Evaluating Adversarial Robustness of Large Vision-Language Models** | [Github](https://github.com/yunqing-me/AttackVLM) * Yunqing Zhao, Tianyu Pang, Chao Du, Xiao Yang, Chongxuan Li, Ngai-Man Cheung, Min Lin * Singapore University of Technology and Design, Sea AI Lab, Tsinghua University, Renmin University of China * [NeurIPs2023] https://arxiv.org/abs/2305.16934 * **VLATTACK: Multimodal Adversarial Attacks on Vision-Language Tasks via Pre-trained Models** | [Github](https://github.com/ericyinyzy/VLAttack) * Ziyi Yin, Muchao Ye, Tianrong Zhang, Tianyu Du, Jinguo Zhu, Han Liu, Jinghui Chen, Ting Wang, Fenglong Ma * The Pennsylvania State University, Zhejiang University, Xi’an Jiaotong University, Dalian University of Technology, Stony Brook University * [NeurIPs2023] [https://arxiv.org/abs/2312.03777](https://arxiv.org/abs/2310.04655) * **Adversarial Illusions in Multi-Modal Embeddings** | [Github](https://github.com/ebagdasa/adversarial_illusions) * Tingwei Zhang, Rishi Jha, Eugene Bagdasaryan, Vitaly Shmatikov * Cornell University, Cornell Tech * [Arxiv2023] https://arxiv.org/abs/2308.11804 * **Image Hijacks: Adversarial Images can Control Generative Models at Runtime** | [Github](https://github.com/euanong/image-hijacks) * Luke Bailey, Euan Ong, Stuart Russell, Scott Emmons * UC Berkeley, Harvard University, University of Cambridge * [Arxiv2023] https://arxiv.org/abs/2309.00236 * **How Robust is Google's Bard to Adversarial Image Attacks?** | [Github](https://github.com/thu-ml/Attack-Bard) * Yinpeng Dong, Huanran Chen, Jiawei Chen, Zhengwei Fang, Xiao Yang, Yichi Zhang, Yu Tian, Hang Su, Jun Zhu * Tsinghua University, RealAI * [Arxiv2023] https://arxiv.org/abs/2309.11751 * **Misusing Tools in Large Language Models With Visual Adversarial Examples** | * Xiaohan Fu, Zihan Wang, Shuheng Li, Rajesh K. G
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